This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
You can also embed plots, for example:
## Warning: package 'plotly' was built under R version 3.6.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:plotly':
##
## arrange, mutate, rename, summarise
## Warning: package 'flexdashboard' was built under R version 3.6.3
## Warning: package 'sf' was built under R version 3.6.3
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
## Warning: package 'htmlwidgets' was built under R version 3.6.3
library(reshape2)
names(gdb2)[names(gdb2)=="neoMR"]<- "Neonates"
names(gdb2)[names(gdb2)=="age1_5MR"]<- "Age 1 to 5 "
names(gdb2)[names(gdb2)=="postneoMR"]<- "Post Neonatles"
names(gdb2)[names(gdb2)=="under5MR"]<- "Under 5 years old"
gdbL=melt(gdb2, # all the data
id.vars = 'gbdRegion') # unique id per row
library(ggplot2)
base = ggplot(data = gdbL, aes(x = variable,
y =gbdRegion))
heat1= base + geom_tile(aes(fill = value))
#inverse color -1
heat2 = heat1 + scale_fill_gradient(low = 'grey',
high = "black")
# change in REORDER
base= ggplot(data = gdbL, aes(x = reorder(variable,
value, median),
y =reorder(gbdRegion,
value, median)))
plot7=base + geom_tile(aes(fill = value)) + labs(title = "Cases of Infants Mortality by World Bank Regions in 2010", caption ="Source :World Bank 2010") + scale_fill_gradient(low = 'grey90',high = "grey50") +
theme(legend.title=element_text(size=14),legend.text=element_text(size=13),plot.caption = element_text(vjust= -0.5, hjust = 1.7, size = 10, face = "bold"),plot.title = element_text(hjust = 0.5, size = 12, face = "bold"), axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 0.5,size = 11,face = 'bold'),
axis.text.y = element_text(size = 12,face = 'bold'))+ labs(fill=' Infants Mortality cases per 100.K ',
x =NULL,
y = NULL)
plot7
# absolute values
absoluteT=table(gdb$gbdRegion,
exclude = 'nothing') #include all!
prop=prop.table(absoluteT)
ToPlot=prop*100
# as data frame
tableFreq=as.data.frame(ToPlot)
names(tableFreq)=c("Region","proportion")
library(ggplot2)
base= ggplot(data = tableFreq,
aes(x = Region,
y = proportion))+ theme_classic()
plot1 = base + geom_bar(fill ="blue",
stat = 'identity') +
coord_flip()
titleText='Distribution of countries by World Bank Regions'
sourceText='Source: World Bank'
plot2 = plot1 + labs(title=titleText,
x =NULL,
y = NULL,
caption = sourceText)
plot3 = plot2 + geom_hline(yintercept = 5.88, #where
linetype="dashed",
size=1, #thickness
alpha=0.5) #transparency
library(scales) # for "unit_format""
## Warning: package 'scales' was built under R version 3.6.2
# customize Y axi
plot4 = plot3 + scale_y_continuous(breaks=c(0,2,4,6,8,10,12),
limits = c(0,12),
labels=unit_format(suffix = '%'))
plot5 = plot4 + theme(plot.caption = element_text(hjust = 1),
plot.title = element_text(hjust = 0.5))
P=paste0(round(tableFreq$proportion,2), '%')
LABELS=P
plot6 = plot5 + geom_text(vjust=0.5, #hjust if flipping
size = 1,
aes(y = proportion ,
label = LABELS))
tableFreq=tableFreq[order(tableFreq$proportion),]
regionOrd=tableFreq[order(tableFreq$proportion),'Region']
LABELS=paste0(round(tableFreq$proportion,2), '%')
base= ggplot(data = tableFreq,
aes(x = region,
y = proportion))
base= base + scale_x_discrete(limits=regionOrd)
base= base +theme_classic()
base= ggplot(data = tableFreq,
aes(x = Region,
y = proportion))
base = base + scale_x_discrete(limits=regionOrd)
plot1 = base + geom_bar(fill ="blue",
stat = 'identity') +
coord_flip()
titleText='Distribution of countries by World Bank Regions'
sourceText='Source: World Bank'
plot2 = plot1 + labs(title=titleText,
x =NULL,
y = NULL,
caption = sourceText)
plot3 = plot2
plot4 = plot3 + scale_y_continuous(breaks=c(0,2,4,6,8,10,12),
limits = c(0,13),
labels=unit_format(suffix = '%'))
plot5 = plot4 + theme(plot.caption = element_text(hjust = 1,face = 'bold', size = 10),
plot.title = element_text(hjust = 0.5, size = 15, face = 'bold'))+ theme(axis.text.x = element_text(angle = 0,
hjust = 0.55,
size = 15, face = "bold"),
axis.text.y = element_text(size = 13, face = "bold"))
plot6 = plot5 + geom_text(vjust=0.5,hjust=-0.1, #hjust if flipping
size = 5,
aes(y = proportion ,
label = LABELS))
plot6 #+ coord_flip() # wanna flip the plot?
linkCSV='https://github.com/pubpolicy/PubPolicy-543/raw/main/gbdChildMortality_2010s.csv'
dataCSV=read.csv(linkCSV)
location="https://github.com/pubpolicy/PubPolicy-543/raw/main/"
file="WB_countries_Admin0_10m.json"
linkToFile=paste0(location,file)
# import
library(sf)
mapWorld=read_sf(linkToFile)
mapWorldVars=merge(mapWorld, #map first
dataCSV,
by.x='ISO_A3', by.y='iso')
library(ggplot2)
# plot original map
base=ggplot(data=mapWorld) + geom_sf(fill='grey90',
color=NA) + theme_classic()
titleText='Distribution of countries by World Bank Economy Classification'
sourceText='Source: World Bank'
colMap= base + geom_sf(data=mapWorldVars,
aes(fill=ECONOMY),
color=NA) + labs(title=titleText,
x =NULL,
y = NULL,
subtitle = "Source: World Bank 2010")+ theme(legend.title.align = 0.5,legend.title=element_text(margin = margin(t = 10),size=10, face = "bold"),legend.text=element_text(size=10, face = "bold"),plot.caption = element_text(hjust = 2,face = 'bold',size = 10),
plot.title = element_text(hjust = 1, size = 15,face = "bold"),legend.key.size = unit(0.7, "cm"))+ theme(axis.text.x = element_text(angle = 0,
hjust = 1,
size = 15, face = "bold"),
axis.text.y = element_text(size = 15, face = "bold"))
colMap
library(htmlwidgets)
base= ggplot(data = gdbL, aes(x = reorder(variable,
value, median),
y =reorder(gbdRegion,
value, median)))
plot10 =base + geom_tile(aes(fill = value)) + labs(title = "Cases of Infants Mortality by World Bank Regions in 2010", caption ="Source :World Bank 2010")
Plot11 = plot10 + scale_fill_gradient(low = 'grey90',high = "grey50")
plot12 = Plot11 + theme(legend.title=element_text(size=0),legend.text=element_text(size=0),plot.caption = element_text(hjust = 1.3, size = 7, face = "bold"),plot.title = element_text(hjust = 0.5, size = 11, face = "bold"), axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 0.5,size = 10,face = 'bold'),
axis.text.y = element_text(size = 8,face = 'bold'))
plot7= plot12 + labs(fill=' Infants Mortality cases in 2010 ',
x =NULL,
y = NULL)
plot7%>%ggplotly()
## Warning in matrix(g$fill_plotlyDomain, nrow = length(y), ncol = length(x), :
## data length [748] is not a sub-multiple or multiple of the number of rows [21]
## Warning in matrix(g$hovertext, nrow = length(y), ncol = length(x), byrow =
## TRUE): data length [748] is not a sub-multiple or multiple of the number of rows
## [21]
plot5 = plot4 + theme(plot.caption = element_text(hjust = 3,face = 'bold', size = 8),
plot.title = element_text(hjust = 0.5, size = 15, face = 'bold'))+ theme(axis.text.x = element_text(angle = 0,
hjust = 3,
size = 12, face = "bold"),
axis.text.y = element_text(size = 13, face = "bold"))
plot7 = plot5 + geom_text(vjust=0.5,hjust= 4, #hjust if flipping
size = 4,
aes(y = proportion ,
label = LABELS))
#+ coord_flip() # wanna flip the plot?
plot7%>%ggplotly()
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)
library(sf)
library(htmlwidgets)
linkCSV='https://github.com/pubpolicy/PubPolicy-543/raw/main/COVID-19%20cases%20and%20testing%20by%20County.csv'
dataCSV=read.csv(linkCSV)
location="https://github.com/pubpolicy/PubPolicy-543/raw/main/"
file="WA_County_Boundaries.json"
linkToFile=paste0(location,file)
# import
library(sf)
mapCounty=read_sf(linkToFile)
mapWorldVars=merge(mapCounty, #map first
dataCSV,
by.x='JURISDIC_2',by.y='JURLBL')
library(ggplot2)
# plot original map
base=ggplot(data=mapCounty) + geom_sf(fill='grey90',
color=NA) + theme_classic()
titleText='COVID-19 testing rates in Washington State by county'
sourceText='Source: Washington Department of Natural Resource'
colMap= base + geom_sf(data=mapWorldVars,
aes(fill= Average.daily.COVID.19.testing.rate.per.100K.people),
color='black') + labs(title=titleText,subtitle = sourceText,
x =NULL,
y = NULL,
caption = sourceText)+ scale_fill_gradient(low = 'blue',
high= 'red')
colMap2= colMap + theme(legend.title=element_text(size=10, face = 'bold'),plot.title = element_text(vjust = -80, hjust = 10, size = 13, face = "bold"))
colMap2%>%ggplotly()
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.